The present invention relates to automatic liver segmentation in medical images, and more particularly, to deep-learning based automatic liver segmentation in 3D medical images.
Accurate liver segmentation in three dimensional (3D) medical images, such as computed tomography (CT) or magnetic resonance (MR) images, is important in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, automatic liver segmentation in medical images is a highly challenging task due to complex background, fuzzy boundaries, and varying appearance of the liver in medical images.
Various methods have been proposed for computer-based automatic liver segmentation from 3D CT scans. Such methods can be generally categorized into non-learning-based and learning-based approaches. Non-learning-based approaches usually rely on the statistical distribution of the intensity, and examples of non-learning-based approaches include atlas-based, active shape model (ASM)-based, level set-based, and graph cut-based segmentation methods. Learning-based approaches typically take advantage of handcrafted features to train machine-learning based classifiers to perform the liver segmentation. However, due to the challenges of liver segmentation, such as complex background, fuzzy boundaries, and varying appearance of the liver in medical images, the existing approaches cannot always provide accurate liver segmentation results. Accordingly, a method for accurate computer-based automatic liver segmentation in medical images is desirable.